{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CGNN Example on the Flow Cytometry dataset dataset \n",
    "Sachs, K., Perez, O., Pe’er, D., Lauffenburger, D. A., & Nolan, G. P. (2005). Causal protein-signaling networks derived from multiparameter single-cell data. Science, 308(5721), 523-529"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Detecting CUDA device(s) : (0, 1, 2, 3)\n",
      "numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n"
     ]
    }
   ],
   "source": [
    "#Import libraries\n",
    "import cdt\n",
    "from cdt import SETTINGS\n",
    "SETTINGS.verbose=False\n",
    "SETTINGS.NJOBS=16\n",
    "import networkx as nx\n",
    "import time\n",
    "# A warning on R libraries might occur. It is for the use of the r libraries that could be imported into the framework\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n",
      "\n",
      "The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>praf</th>\n",
       "      <th>pmek</th>\n",
       "      <th>plcg</th>\n",
       "      <th>PIP2</th>\n",
       "      <th>PIP3</th>\n",
       "      <th>p44/42</th>\n",
       "      <th>pakts473</th>\n",
       "      <th>PKA</th>\n",
       "      <th>PKC</th>\n",
       "      <th>P38</th>\n",
       "      <th>pjnk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26.4</td>\n",
       "      <td>13.2</td>\n",
       "      <td>8.82</td>\n",
       "      <td>18.30</td>\n",
       "      <td>58.80</td>\n",
       "      <td>6.61</td>\n",
       "      <td>17.0</td>\n",
       "      <td>414.0</td>\n",
       "      <td>17.00</td>\n",
       "      <td>44.9</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.9</td>\n",
       "      <td>16.5</td>\n",
       "      <td>12.30</td>\n",
       "      <td>16.80</td>\n",
       "      <td>8.13</td>\n",
       "      <td>18.60</td>\n",
       "      <td>32.5</td>\n",
       "      <td>352.0</td>\n",
       "      <td>3.37</td>\n",
       "      <td>16.5</td>\n",
       "      <td>61.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>59.4</td>\n",
       "      <td>44.1</td>\n",
       "      <td>14.60</td>\n",
       "      <td>10.20</td>\n",
       "      <td>13.00</td>\n",
       "      <td>14.90</td>\n",
       "      <td>32.5</td>\n",
       "      <td>403.0</td>\n",
       "      <td>11.40</td>\n",
       "      <td>31.9</td>\n",
       "      <td>19.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>73.0</td>\n",
       "      <td>82.8</td>\n",
       "      <td>23.10</td>\n",
       "      <td>13.50</td>\n",
       "      <td>1.29</td>\n",
       "      <td>5.83</td>\n",
       "      <td>11.8</td>\n",
       "      <td>528.0</td>\n",
       "      <td>13.70</td>\n",
       "      <td>28.6</td>\n",
       "      <td>23.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33.7</td>\n",
       "      <td>19.8</td>\n",
       "      <td>5.19</td>\n",
       "      <td>9.73</td>\n",
       "      <td>24.80</td>\n",
       "      <td>21.10</td>\n",
       "      <td>46.1</td>\n",
       "      <td>305.0</td>\n",
       "      <td>4.66</td>\n",
       "      <td>25.7</td>\n",
       "      <td>81.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   praf  pmek   plcg   PIP2   PIP3  p44/42  pakts473    PKA    PKC   P38  pjnk\n",
       "0  26.4  13.2   8.82  18.30  58.80    6.61      17.0  414.0  17.00  44.9  40.0\n",
       "1  35.9  16.5  12.30  16.80   8.13   18.60      32.5  352.0   3.37  16.5  61.5\n",
       "2  59.4  44.1  14.60  10.20  13.00   14.90      32.5  403.0  11.40  31.9  19.5\n",
       "3  73.0  82.8  23.10  13.50   1.29    5.83      11.8  528.0  13.70  28.6  23.1\n",
       "4  33.7  19.8   5.19   9.73  24.80   21.10      46.1  305.0   4.66  25.7  81.3"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load data and graph solution\n",
    "data, solution = cdt.data.load_dataset('sachs')\n",
    "nx.draw_networkx(solution, font_size=8) # The plot function allows for quick visualization of the graph. \n",
    "plt.show()\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Execution time : 575. seconds ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "The is_numlike function was deprecated in Matplotlib 3.0 and will be removed in 3.2. Use isinstance(..., numbers.Number) instead.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>praf</td>\n",
       "      <td>PIP3</td>\n",
       "      <td>3.648269e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>praf</td>\n",
       "      <td>pakts473</td>\n",
       "      <td>7.585697e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>praf</td>\n",
       "      <td>P38</td>\n",
       "      <td>1.438171e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PIP3</td>\n",
       "      <td>PKC</td>\n",
       "      <td>5.694801e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PIP3</td>\n",
       "      <td>pjnk</td>\n",
       "      <td>6.234893e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>pakts473</td>\n",
       "      <td>PIP2</td>\n",
       "      <td>1.649825e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pakts473</td>\n",
       "      <td>PKA</td>\n",
       "      <td>3.306730e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>pakts473</td>\n",
       "      <td>pjnk</td>\n",
       "      <td>1.564420e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>P38</td>\n",
       "      <td>pmek</td>\n",
       "      <td>1.251111e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>P38</td>\n",
       "      <td>plcg</td>\n",
       "      <td>1.025868e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>P38</td>\n",
       "      <td>PKC</td>\n",
       "      <td>1.681802e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>P38</td>\n",
       "      <td>pjnk</td>\n",
       "      <td>1.970683e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>pmek</td>\n",
       "      <td>p44/42</td>\n",
       "      <td>1.576390e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>pmek</td>\n",
       "      <td>pjnk</td>\n",
       "      <td>4.560272e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>p44/42</td>\n",
       "      <td>plcg</td>\n",
       "      <td>1.186375e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>p44/42</td>\n",
       "      <td>PIP2</td>\n",
       "      <td>1.538710e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>p44/42</td>\n",
       "      <td>PKC</td>\n",
       "      <td>1.528148e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>pjnk</td>\n",
       "      <td>plcg</td>\n",
       "      <td>1.752370e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>pjnk</td>\n",
       "      <td>PKC</td>\n",
       "      <td>6.930091e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>PIP2</td>\n",
       "      <td>PKC</td>\n",
       "      <td>4.119725e-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1             2\n",
       "0       praf      PIP3  3.648269e-07\n",
       "1       praf  pakts473  7.585697e-05\n",
       "2       praf       P38  1.438171e-07\n",
       "3       PIP3       PKC  5.694801e-07\n",
       "4       PIP3      pjnk  6.234893e-06\n",
       "5   pakts473      PIP2  1.649825e-07\n",
       "6   pakts473       PKA  3.306730e-05\n",
       "7   pakts473      pjnk  1.564420e-05\n",
       "8        P38      pmek  1.251111e-07\n",
       "9        P38      plcg  1.025868e-07\n",
       "10       P38       PKC  1.681802e-07\n",
       "11       P38      pjnk  1.970683e-05\n",
       "12      pmek    p44/42  1.576390e-06\n",
       "13      pmek      pjnk  4.560272e-05\n",
       "14    p44/42      plcg  1.186375e-07\n",
       "15    p44/42      PIP2  1.538710e-07\n",
       "16    p44/42       PKC  1.528148e-07\n",
       "17      pjnk      plcg  1.752370e-05\n",
       "18      pjnk       PKC  6.930091e-06\n",
       "19      PIP2       PKC  4.119725e-07"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the structure of the graph\n",
    "from cdt.independence.graph import FSGNN\n",
    "\n",
    "Fsgnn = FSGNN(train_epochs=1000, test_epochs=500, l1=0.1, batch_size=1000)\n",
    "\n",
    "start_time = time.time()\n",
    "ugraph = Fsgnn.predict(data, threshold=1e-7)\n",
    "print(\"--- Execution time : %4.4s seconds ---\" % (time.time() - start_time))\n",
    "nx.draw_networkx(ugraph, font_size=8) # The plot function allows for quick visualization of the graph.\n",
    "plt.show()\n",
    "# List results\n",
    "pd.DataFrame(list(ugraph.edges(data='weight')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Execution time : 1309 seconds ---\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cause</th>\n",
       "      <th>Effect</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PIP3</td>\n",
       "      <td>praf</td>\n",
       "      <td>0.082269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PIP3</td>\n",
       "      <td>PKC</td>\n",
       "      <td>0.175768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PIP3</td>\n",
       "      <td>pjnk</td>\n",
       "      <td>0.060107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>praf</td>\n",
       "      <td>P38</td>\n",
       "      <td>0.250264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>pakts473</td>\n",
       "      <td>praf</td>\n",
       "      <td>0.166273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>pakts473</td>\n",
       "      <td>PIP2</td>\n",
       "      <td>0.010147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pakts473</td>\n",
       "      <td>pjnk</td>\n",
       "      <td>0.109217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>P38</td>\n",
       "      <td>plcg</td>\n",
       "      <td>0.117087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PKC</td>\n",
       "      <td>P38</td>\n",
       "      <td>0.080231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>pjnk</td>\n",
       "      <td>plcg</td>\n",
       "      <td>0.022892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>pjnk</td>\n",
       "      <td>PKC</td>\n",
       "      <td>0.143263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>pjnk</td>\n",
       "      <td>P38</td>\n",
       "      <td>0.051328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>PIP2</td>\n",
       "      <td>PKC</td>\n",
       "      <td>0.133282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>PKA</td>\n",
       "      <td>pakts473</td>\n",
       "      <td>0.217405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>pmek</td>\n",
       "      <td>P38</td>\n",
       "      <td>0.078513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>pmek</td>\n",
       "      <td>pjnk</td>\n",
       "      <td>0.094474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>p44/42</td>\n",
       "      <td>pmek</td>\n",
       "      <td>0.182682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>p44/42</td>\n",
       "      <td>plcg</td>\n",
       "      <td>0.139812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>p44/42</td>\n",
       "      <td>PIP2</td>\n",
       "      <td>0.090631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>p44/42</td>\n",
       "      <td>PKC</td>\n",
       "      <td>0.211409</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Cause    Effect     Score\n",
       "0       PIP3      praf  0.082269\n",
       "1       PIP3       PKC  0.175768\n",
       "2       PIP3      pjnk  0.060107\n",
       "3       praf       P38  0.250264\n",
       "4   pakts473      praf  0.166273\n",
       "5   pakts473      PIP2  0.010147\n",
       "6   pakts473      pjnk  0.109217\n",
       "7        P38      plcg  0.117087\n",
       "8        PKC       P38  0.080231\n",
       "9       pjnk      plcg  0.022892\n",
       "10      pjnk       PKC  0.143263\n",
       "11      pjnk       P38  0.051328\n",
       "12      PIP2       PKC  0.133282\n",
       "13       PKA  pakts473  0.217405\n",
       "14      pmek       P38  0.078513\n",
       "15      pmek      pjnk  0.094474\n",
       "16    p44/42      pmek  0.182682\n",
       "17    p44/42      plcg  0.139812\n",
       "18    p44/42      PIP2  0.090631\n",
       "19    p44/42       PKC  0.211409"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pairwise orientation of the edges of the graph\n",
    "from cdt.causality.pairwise import GNN\n",
    "from cdt.utils.graph import dagify_min_edge\n",
    "start_time = time.time()\n",
    "\n",
    "gnn = GNN(nruns=32, train_epochs=1000, test_epochs=500, batch_size=1000)\n",
    "ograph = dagify_min_edge(gnn.orient_graph(data, ugraph))\n",
    "print(\"--- Execution time : %4.4s seconds ---\" % (time.time() - start_time))\n",
    "nx.draw_networkx(ugraph, font_size=8) # The plot function allows for quick visualization of the graph.\n",
    "plt.show()\n",
    "# List results\n",
    "pd.DataFrame(list(ograph.edges(data='weight')), columns=['Cause', 'Effect', 'Score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Execution time : 1080 seconds ---\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cause</th>\n",
       "      <th>Effect</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PIP3</td>\n",
       "      <td>praf</td>\n",
       "      <td>0.082269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PIP3</td>\n",
       "      <td>PKC</td>\n",
       "      <td>0.175768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PIP3</td>\n",
       "      <td>pjnk</td>\n",
       "      <td>0.060107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>praf</td>\n",
       "      <td>P38</td>\n",
       "      <td>0.250264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>pakts473</td>\n",
       "      <td>praf</td>\n",
       "      <td>0.166273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>pakts473</td>\n",
       "      <td>PIP2</td>\n",
       "      <td>0.010147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pakts473</td>\n",
       "      <td>pjnk</td>\n",
       "      <td>0.109217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>P38</td>\n",
       "      <td>plcg</td>\n",
       "      <td>0.117087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PKC</td>\n",
       "      <td>P38</td>\n",
       "      <td>0.080231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>pjnk</td>\n",
       "      <td>plcg</td>\n",
       "      <td>0.022892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>pjnk</td>\n",
       "      <td>PKC</td>\n",
       "      <td>0.143263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>pjnk</td>\n",
       "      <td>P38</td>\n",
       "      <td>0.051328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>PIP2</td>\n",
       "      <td>PKC</td>\n",
       "      <td>0.133282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>PKA</td>\n",
       "      <td>pakts473</td>\n",
       "      <td>0.217405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>pmek</td>\n",
       "      <td>P38</td>\n",
       "      <td>0.078513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>pmek</td>\n",
       "      <td>pjnk</td>\n",
       "      <td>0.094474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>p44/42</td>\n",
       "      <td>pmek</td>\n",
       "      <td>0.182682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>p44/42</td>\n",
       "      <td>plcg</td>\n",
       "      <td>0.139812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>p44/42</td>\n",
       "      <td>PIP2</td>\n",
       "      <td>0.090631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>p44/42</td>\n",
       "      <td>PKC</td>\n",
       "      <td>0.211409</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Cause    Effect     Score\n",
       "0       PIP3      praf  0.082269\n",
       "1       PIP3       PKC  0.175768\n",
       "2       PIP3      pjnk  0.060107\n",
       "3       praf       P38  0.250264\n",
       "4   pakts473      praf  0.166273\n",
       "5   pakts473      PIP2  0.010147\n",
       "6   pakts473      pjnk  0.109217\n",
       "7        P38      plcg  0.117087\n",
       "8        PKC       P38  0.080231\n",
       "9       pjnk      plcg  0.022892\n",
       "10      pjnk       PKC  0.143263\n",
       "11      pjnk       P38  0.051328\n",
       "12      PIP2       PKC  0.133282\n",
       "13       PKA  pakts473  0.217405\n",
       "14      pmek       P38  0.078513\n",
       "15      pmek      pjnk  0.094474\n",
       "16    p44/42      pmek  0.182682\n",
       "17    p44/42      plcg  0.139812\n",
       "18    p44/42      PIP2  0.090631\n",
       "19    p44/42       PKC  0.211409"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from cdt.causality.graph import CGNN\n",
    "Cgnn = CGNN(nruns=16, train_epochs=2000, test_epochs=1000, batch_size=1000)\n",
    "start_time = time.time()\n",
    "dgraph = Cgnn.orient_directed_graph(data, ograph)\n",
    "print(\"--- Execution time : %4.4s seconds ---\" % (time.time() - start_time))\n",
    "\n",
    "# Plot the output graph\n",
    "nx.draw_networkx(dgraph, font_size=8) # The plot function allows for quick visualization of the graph.\n",
    "plt.show() \n",
    "# Print output results : \n",
    "pd.DataFrame(list(dgraph.edges(data='weight')), columns=['Cause', 'Effect', 'Score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
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